Bendale and Boult [1]} identified unknowns in the feature space of deep networks and used a Weibull distribution to estimate the set risk (called OpenMax classifier). A generative version of OpenMax was proposed in [2]} by synthesizing novel class images. Liu [3]} considered a long-tailed recognition setting where majority, minority and unknown classes coexist. They developed a metric learning framework identify unseen classes as unknown. In similar spirit, several dedicated approaches target on detecting the out of distribution samples [4]} or novelties [5]}. Recently, self-supervised learning [6]} and unsupervised learning with reconstruction [7]} have been explored for open set recognition. However, while these works can recognize unknown instances, they cannot dynamically update themselves in an incremental fashion over multiple training episodes. Further, our energy based unknown detection approach has not been explored before.
